The objective of this research is to develop robust tools to process noninvasively measured physiological signals for hypovolemia estimation in out-of-hospital settings. Hypovolemia, or low blood volume, is a leading cause of preventable death in many out-of-hospital scenarios. A critical period to reduce hypovolemia-related deaths is during patient transport to medical facilities. With the timely nature of severe hypovolemia, accurate, real-time assessment of its progression can enable medical responders to promptly administer prehospital treatment. Previous research has shown that the seismocardiogram (SCG), which monitors cardiomechanical function noninvasively, can estimate hypovolemia progression more effectively than traditional vital signs. However, SCG signals are highly susceptible to vibration artifacts, which limits their use in prehospital settings where accurate and timely responses are essential. A key goal of this dissertation is to enable SCG processing in noisy environments through robust denoising and feature extraction tools that can operate in real-time or online settings. We first explored real-time SCG feature extraction, demonstrating that shared SCG signal dynamics can be leveraged to enhance real-time SCG feature tracking in noisy environments. We then investigated the effects of vehicular vibrations on SCG signals, designing a signal decomposition framework that used the SCG periodic structure to separate the two. Finally, we utilize generative diffusion models to learn the latent space of SCG beats and enhance SCG signals collected in ambulatory settings. The tools developed in this work aim to advance hypovolemia monitoring systems that utilize these physiological signals by making them suitable for noisy prehospital care and daily life settings.